RRepoGEO

REPOGEO REPORT · LITE

IDEA-Research/Rex-Omni

Default branch master · commit 6508981c · scanned 5/12/2026, 2:13:13 AM

GitHub: 1,343 stars · 90 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface IDEA-Research/Rex-Omni, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Refine the 'about' description to clearly state the core innovation

    Why:

    CURRENT
    [CVPR2026] Detect Anything via Next Point Prediction
    COPY-PASTE FIX
    [CVPR2026] Rex-Omni: A 3B-parameter Multimodal Large Language Model (MLLM) that redefines object detection and diverse visual perception tasks as a simple next-token prediction problem.
  • hightopics#2
    Add topics that highlight the unique methodology

    Why:

    CURRENT
    mllm, object-detection, open-set
    COPY-PASTE FIX
    mllm, object-detection, open-set, next-token-prediction, generative-ai-for-vision
  • mediumreadme#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    ## License
This project is released under the specific terms outlined in the [LICENSE file](LICENSE). Please review the file for full details on usage and distribution.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface IDEA-Research/Rex-Omni
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Grounded-SAM
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Grounded-SAM · recommended 1×
  2. Grounding DINO · recommended 1×
  3. Segment Anything Model (SAM) · recommended 1×
  4. OWL-ViT · recommended 1×
  5. CLIP · recommended 1×
  • CATEGORY QUERY
    How to implement open-set object detection using a multimodal large language model?
    you: not recommended
    AI recommended (in order):
    1. Grounded-SAM
    2. Grounding DINO
    3. Segment Anything Model (SAM)
    4. OWL-ViT
    5. CLIP
    6. Faster R-CNN
    7. Mask R-CNN
    8. YOLO
    9. DETR
    10. Llama-Adapter V2
    11. Llama
    12. OWL-ViT-V2

    AI recommended 12 alternatives but never named IDEA-Research/Rex-Omni. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What MLLMs are available for diverse visual perception tasks via next-token prediction?
    you: not recommended
    AI recommended (in order):
    1. GPT-4o
    2. Gemini
    3. LLaVA (haotian-liu/LLaVA)
    4. CogVLM (THUDM/CogVLM)
    5. Fuyu-8B (adept-ai/fuyu-8b)

    AI recommended 5 alternatives but never named IDEA-Research/Rex-Omni. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of IDEA-Research/Rex-Omni?
    pass
    AI named IDEA-Research/Rex-Omni explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts IDEA-Research/Rex-Omni in production, what risks or prerequisites should they evaluate first?
    pass
    AI named IDEA-Research/Rex-Omni explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo IDEA-Research/Rex-Omni solve, and who is the primary audience?
    pass
    AI named IDEA-Research/Rex-Omni explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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IDEA-Research/Rex-Omni — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite